Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Friday 18 September 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 249,477 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… miss… e380000… nhs_glo… 1 gl34fe South West
## [90m 2[39m 111 2020-03-18 fema… miss… e380001… nhs_sou… 1 ne325nn North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_air… 8 bd57jr North Eas…
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ash… 7 tn254ab South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 9 n111np London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 11 s752py North Eas…
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 19 ss143hg East of E…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 6 dn227xf North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bat… 9 ba25rp South West
## [90m# … with 249,467 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 65
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 100
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 14
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 7
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 1
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 8
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 4
## 113 2020-06-21 East of England 3
## 114 2020-06-22 East of England 6
## 115 2020-06-23 East of England 5
## 116 2020-06-24 East of England 4
## 117 2020-06-25 East of England 1
## 118 2020-06-26 East of England 5
## 119 2020-06-27 East of England 6
## 120 2020-06-28 East of England 8
## 121 2020-06-29 East of England 4
## 122 2020-06-30 East of England 5
## 123 2020-07-01 East of England 2
## 124 2020-07-02 East of England 5
## 125 2020-07-03 East of England 0
## 126 2020-07-04 East of England 3
## 127 2020-07-05 East of England 1
## 128 2020-07-06 East of England 2
## 129 2020-07-07 East of England 2
## 130 2020-07-08 East of England 0
## 131 2020-07-09 East of England 8
## 132 2020-07-10 East of England 4
## 133 2020-07-11 East of England 2
## 134 2020-07-12 East of England 1
## 135 2020-07-13 East of England 8
## 136 2020-07-14 East of England 2
## 137 2020-07-15 East of England 0
## 138 2020-07-16 East of England 0
## 139 2020-07-17 East of England 0
## 140 2020-07-18 East of England 0
## 141 2020-07-19 East of England 1
## 142 2020-07-20 East of England 1
## 143 2020-07-21 East of England 1
## 144 2020-07-22 East of England 2
## 145 2020-07-23 East of England 1
## 146 2020-07-24 East of England 1
## 147 2020-07-25 East of England 0
## 148 2020-07-26 East of England 1
## 149 2020-07-27 East of England 1
## 150 2020-07-28 East of England 2
## 151 2020-07-29 East of England 0
## 152 2020-07-30 East of England 0
## 153 2020-07-31 East of England 1
## 154 2020-08-01 East of England 0
## 155 2020-08-02 East of England 0
## 156 2020-08-03 East of England 0
## 157 2020-08-04 East of England 1
## 158 2020-08-05 East of England 1
## 159 2020-08-06 East of England 0
## 160 2020-08-07 East of England 1
## 161 2020-08-08 East of England 0
## 162 2020-08-09 East of England 0
## 163 2020-08-10 East of England 1
## 164 2020-08-11 East of England 2
## 165 2020-08-12 East of England 1
## 166 2020-08-13 East of England 0
## 167 2020-08-14 East of England 0
## 168 2020-08-15 East of England 1
## 169 2020-08-16 East of England 0
## 170 2020-08-17 East of England 0
## 171 2020-08-18 East of England 2
## 172 2020-08-19 East of England 1
## 173 2020-08-20 East of England 1
## 174 2020-08-21 East of England 0
## 175 2020-08-22 East of England 1
## 176 2020-08-23 East of England 1
## 177 2020-08-24 East of England 0
## 178 2020-08-25 East of England 0
## 179 2020-08-26 East of England 1
## 180 2020-08-27 East of England 1
## 181 2020-08-28 East of England 0
## 182 2020-08-29 East of England 0
## 183 2020-08-30 East of England 0
## 184 2020-08-31 East of England 0
## 185 2020-09-01 East of England 0
## 186 2020-09-02 East of England 0
## 187 2020-09-03 East of England 1
## 188 2020-09-04 East of England 1
## 189 2020-09-05 East of England 0
## 190 2020-09-06 East of England 1
## 191 2020-09-07 East of England 0
## 192 2020-09-08 East of England 0
## 193 2020-09-09 East of England 0
## 194 2020-09-10 East of England 0
## 195 2020-09-11 East of England 0
## 196 2020-09-12 East of England 0
## 197 2020-09-13 East of England 1
## 198 2020-09-14 East of England 1
## 199 2020-09-15 East of England 0
## 200 2020-09-16 East of England 0
## 201 2020-03-01 London 0
## 202 2020-03-02 London 0
## 203 2020-03-03 London 0
## 204 2020-03-04 London 0
## 205 2020-03-05 London 0
## 206 2020-03-06 London 1
## 207 2020-03-07 London 0
## 208 2020-03-08 London 0
## 209 2020-03-09 London 1
## 210 2020-03-10 London 0
## 211 2020-03-11 London 5
## 212 2020-03-12 London 6
## 213 2020-03-13 London 10
## 214 2020-03-14 London 13
## 215 2020-03-15 London 9
## 216 2020-03-16 London 15
## 217 2020-03-17 London 23
## 218 2020-03-18 London 28
## 219 2020-03-19 London 25
## 220 2020-03-20 London 44
## 221 2020-03-21 London 49
## 222 2020-03-22 London 54
## 223 2020-03-23 London 63
## 224 2020-03-24 London 86
## 225 2020-03-25 London 112
## 226 2020-03-26 London 129
## 227 2020-03-27 London 129
## 228 2020-03-28 London 122
## 229 2020-03-29 London 145
## 230 2020-03-30 London 149
## 231 2020-03-31 London 181
## 232 2020-04-01 London 202
## 233 2020-04-02 London 191
## 234 2020-04-03 London 198
## 235 2020-04-04 London 231
## 236 2020-04-05 London 195
## 237 2020-04-06 London 197
## 238 2020-04-07 London 220
## 239 2020-04-08 London 239
## 240 2020-04-09 London 206
## 241 2020-04-10 London 171
## 242 2020-04-11 London 178
## 243 2020-04-12 London 158
## 244 2020-04-13 London 166
## 245 2020-04-14 London 143
## 246 2020-04-15 London 142
## 247 2020-04-16 London 140
## 248 2020-04-17 London 101
## 249 2020-04-18 London 101
## 250 2020-04-19 London 103
## 251 2020-04-20 London 96
## 252 2020-04-21 London 94
## 253 2020-04-22 London 109
## 254 2020-04-23 London 77
## 255 2020-04-24 London 71
## 256 2020-04-25 London 58
## 257 2020-04-26 London 53
## 258 2020-04-27 London 51
## 259 2020-04-28 London 44
## 260 2020-04-29 London 45
## 261 2020-04-30 London 40
## 262 2020-05-01 London 41
## 263 2020-05-02 London 41
## 264 2020-05-03 London 36
## 265 2020-05-04 London 30
## 266 2020-05-05 London 25
## 267 2020-05-06 London 37
## 268 2020-05-07 London 37
## 269 2020-05-08 London 30
## 270 2020-05-09 London 23
## 271 2020-05-10 London 26
## 272 2020-05-11 London 18
## 273 2020-05-12 London 18
## 274 2020-05-13 London 17
## 275 2020-05-14 London 20
## 276 2020-05-15 London 19
## 277 2020-05-16 London 14
## 278 2020-05-17 London 15
## 279 2020-05-18 London 11
## 280 2020-05-19 London 14
## 281 2020-05-20 London 19
## 282 2020-05-21 London 12
## 283 2020-05-22 London 10
## 284 2020-05-23 London 6
## 285 2020-05-24 London 7
## 286 2020-05-25 London 9
## 287 2020-05-26 London 14
## 288 2020-05-27 London 7
## 289 2020-05-28 London 8
## 290 2020-05-29 London 7
## 291 2020-05-30 London 12
## 292 2020-05-31 London 6
## 293 2020-06-01 London 10
## 294 2020-06-02 London 8
## 295 2020-06-03 London 6
## 296 2020-06-04 London 8
## 297 2020-06-05 London 4
## 298 2020-06-06 London 0
## 299 2020-06-07 London 5
## 300 2020-06-08 London 5
## 301 2020-06-09 London 5
## 302 2020-06-10 London 8
## 303 2020-06-11 London 5
## 304 2020-06-12 London 3
## 305 2020-06-13 London 3
## 306 2020-06-14 London 3
## 307 2020-06-15 London 1
## 308 2020-06-16 London 2
## 309 2020-06-17 London 1
## 310 2020-06-18 London 2
## 311 2020-06-19 London 5
## 312 2020-06-20 London 3
## 313 2020-06-21 London 4
## 314 2020-06-22 London 2
## 315 2020-06-23 London 1
## 316 2020-06-24 London 4
## 317 2020-06-25 London 3
## 318 2020-06-26 London 2
## 319 2020-06-27 London 1
## 320 2020-06-28 London 2
## 321 2020-06-29 London 2
## 322 2020-06-30 London 1
## 323 2020-07-01 London 2
## 324 2020-07-02 London 2
## 325 2020-07-03 London 2
## 326 2020-07-04 London 1
## 327 2020-07-05 London 3
## 328 2020-07-06 London 2
## 329 2020-07-07 London 1
## 330 2020-07-08 London 3
## 331 2020-07-09 London 4
## 332 2020-07-10 London 0
## 333 2020-07-11 London 1
## 334 2020-07-12 London 1
## 335 2020-07-13 London 1
## 336 2020-07-14 London 0
## 337 2020-07-15 London 2
## 338 2020-07-16 London 0
## 339 2020-07-17 London 0
## 340 2020-07-18 London 2
## 341 2020-07-19 London 0
## 342 2020-07-20 London 0
## 343 2020-07-21 London 1
## 344 2020-07-22 London 0
## 345 2020-07-23 London 2
## 346 2020-07-24 London 0
## 347 2020-07-25 London 1
## 348 2020-07-26 London 0
## 349 2020-07-27 London 1
## 350 2020-07-28 London 0
## 351 2020-07-29 London 0
## 352 2020-07-30 London 1
## 353 2020-07-31 London 0
## 354 2020-08-01 London 0
## 355 2020-08-02 London 3
## 356 2020-08-03 London 0
## 357 2020-08-04 London 0
## 358 2020-08-05 London 0
## 359 2020-08-06 London 1
## 360 2020-08-07 London 0
## 361 2020-08-08 London 0
## 362 2020-08-09 London 0
## 363 2020-08-10 London 0
## 364 2020-08-11 London 1
## 365 2020-08-12 London 0
## 366 2020-08-13 London 2
## 367 2020-08-14 London 0
## 368 2020-08-15 London 0
## 369 2020-08-16 London 0
## 370 2020-08-17 London 1
## 371 2020-08-18 London 1
## 372 2020-08-19 London 0
## 373 2020-08-20 London 1
## 374 2020-08-21 London 0
## 375 2020-08-22 London 0
## 376 2020-08-23 London 0
## 377 2020-08-24 London 1
## 378 2020-08-25 London 1
## 379 2020-08-26 London 0
## 380 2020-08-27 London 0
## 381 2020-08-28 London 0
## 382 2020-08-29 London 0
## 383 2020-08-30 London 0
## 384 2020-08-31 London 1
## 385 2020-09-01 London 0
## 386 2020-09-02 London 1
## 387 2020-09-03 London 1
## 388 2020-09-04 London 0
## 389 2020-09-05 London 0
## 390 2020-09-06 London 2
## 391 2020-09-07 London 0
## 392 2020-09-08 London 0
## 393 2020-09-09 London 0
## 394 2020-09-10 London 1
## 395 2020-09-11 London 1
## 396 2020-09-12 London 1
## 397 2020-09-13 London 0
## 398 2020-09-14 London 0
## 399 2020-09-15 London 1
## 400 2020-09-16 London 0
## 401 2020-03-01 Midlands 0
## 402 2020-03-02 Midlands 0
## 403 2020-03-03 Midlands 1
## 404 2020-03-04 Midlands 0
## 405 2020-03-05 Midlands 0
## 406 2020-03-06 Midlands 0
## 407 2020-03-07 Midlands 0
## 408 2020-03-08 Midlands 2
## 409 2020-03-09 Midlands 1
## 410 2020-03-10 Midlands 0
## 411 2020-03-11 Midlands 2
## 412 2020-03-12 Midlands 6
## 413 2020-03-13 Midlands 5
## 414 2020-03-14 Midlands 4
## 415 2020-03-15 Midlands 5
## 416 2020-03-16 Midlands 11
## 417 2020-03-17 Midlands 8
## 418 2020-03-18 Midlands 13
## 419 2020-03-19 Midlands 8
## 420 2020-03-20 Midlands 28
## 421 2020-03-21 Midlands 13
## 422 2020-03-22 Midlands 31
## 423 2020-03-23 Midlands 33
## 424 2020-03-24 Midlands 41
## 425 2020-03-25 Midlands 48
## 426 2020-03-26 Midlands 64
## 427 2020-03-27 Midlands 72
## 428 2020-03-28 Midlands 89
## 429 2020-03-29 Midlands 92
## 430 2020-03-30 Midlands 90
## 431 2020-03-31 Midlands 123
## 432 2020-04-01 Midlands 140
## 433 2020-04-02 Midlands 142
## 434 2020-04-03 Midlands 124
## 435 2020-04-04 Midlands 151
## 436 2020-04-05 Midlands 164
## 437 2020-04-06 Midlands 140
## 438 2020-04-07 Midlands 123
## 439 2020-04-08 Midlands 186
## 440 2020-04-09 Midlands 139
## 441 2020-04-10 Midlands 127
## 442 2020-04-11 Midlands 142
## 443 2020-04-12 Midlands 139
## 444 2020-04-13 Midlands 120
## 445 2020-04-14 Midlands 116
## 446 2020-04-15 Midlands 147
## 447 2020-04-16 Midlands 102
## 448 2020-04-17 Midlands 118
## 449 2020-04-18 Midlands 115
## 450 2020-04-19 Midlands 92
## 451 2020-04-20 Midlands 107
## 452 2020-04-21 Midlands 86
## 453 2020-04-22 Midlands 78
## 454 2020-04-23 Midlands 103
## 455 2020-04-24 Midlands 79
## 456 2020-04-25 Midlands 72
## 457 2020-04-26 Midlands 81
## 458 2020-04-27 Midlands 74
## 459 2020-04-28 Midlands 68
## 460 2020-04-29 Midlands 53
## 461 2020-04-30 Midlands 56
## 462 2020-05-01 Midlands 64
## 463 2020-05-02 Midlands 51
## 464 2020-05-03 Midlands 52
## 465 2020-05-04 Midlands 61
## 466 2020-05-05 Midlands 59
## 467 2020-05-06 Midlands 59
## 468 2020-05-07 Midlands 48
## 469 2020-05-08 Midlands 34
## 470 2020-05-09 Midlands 37
## 471 2020-05-10 Midlands 42
## 472 2020-05-11 Midlands 33
## 473 2020-05-12 Midlands 45
## 474 2020-05-13 Midlands 40
## 475 2020-05-14 Midlands 38
## 476 2020-05-15 Midlands 40
## 477 2020-05-16 Midlands 34
## 478 2020-05-17 Midlands 31
## 479 2020-05-18 Midlands 36
## 480 2020-05-19 Midlands 35
## 481 2020-05-20 Midlands 36
## 482 2020-05-21 Midlands 32
## 483 2020-05-22 Midlands 27
## 484 2020-05-23 Midlands 34
## 485 2020-05-24 Midlands 20
## 486 2020-05-25 Midlands 26
## 487 2020-05-26 Midlands 33
## 488 2020-05-27 Midlands 29
## 489 2020-05-28 Midlands 28
## 490 2020-05-29 Midlands 20
## 491 2020-05-30 Midlands 21
## 492 2020-05-31 Midlands 22
## 493 2020-06-01 Midlands 20
## 494 2020-06-02 Midlands 22
## 495 2020-06-03 Midlands 24
## 496 2020-06-04 Midlands 16
## 497 2020-06-05 Midlands 21
## 498 2020-06-06 Midlands 20
## 499 2020-06-07 Midlands 17
## 500 2020-06-08 Midlands 16
## 501 2020-06-09 Midlands 18
## 502 2020-06-10 Midlands 15
## 503 2020-06-11 Midlands 13
## 504 2020-06-12 Midlands 12
## 505 2020-06-13 Midlands 6
## 506 2020-06-14 Midlands 18
## 507 2020-06-15 Midlands 12
## 508 2020-06-16 Midlands 15
## 509 2020-06-17 Midlands 11
## 510 2020-06-18 Midlands 15
## 511 2020-06-19 Midlands 10
## 512 2020-06-20 Midlands 15
## 513 2020-06-21 Midlands 14
## 514 2020-06-22 Midlands 14
## 515 2020-06-23 Midlands 16
## 516 2020-06-24 Midlands 15
## 517 2020-06-25 Midlands 18
## 518 2020-06-26 Midlands 5
## 519 2020-06-27 Midlands 5
## 520 2020-06-28 Midlands 7
## 521 2020-06-29 Midlands 6
## 522 2020-06-30 Midlands 6
## 523 2020-07-01 Midlands 7
## 524 2020-07-02 Midlands 10
## 525 2020-07-03 Midlands 3
## 526 2020-07-04 Midlands 4
## 527 2020-07-05 Midlands 6
## 528 2020-07-06 Midlands 5
## 529 2020-07-07 Midlands 3
## 530 2020-07-08 Midlands 5
## 531 2020-07-09 Midlands 9
## 532 2020-07-10 Midlands 3
## 533 2020-07-11 Midlands 0
## 534 2020-07-12 Midlands 5
## 535 2020-07-13 Midlands 1
## 536 2020-07-14 Midlands 1
## 537 2020-07-15 Midlands 6
## 538 2020-07-16 Midlands 2
## 539 2020-07-17 Midlands 3
## 540 2020-07-18 Midlands 3
## 541 2020-07-19 Midlands 3
## 542 2020-07-20 Midlands 3
## 543 2020-07-21 Midlands 1
## 544 2020-07-22 Midlands 2
## 545 2020-07-23 Midlands 6
## 546 2020-07-24 Midlands 1
## 547 2020-07-25 Midlands 4
## 548 2020-07-26 Midlands 4
## 549 2020-07-27 Midlands 5
## 550 2020-07-28 Midlands 1
## 551 2020-07-29 Midlands 1
## 552 2020-07-30 Midlands 1
## 553 2020-07-31 Midlands 2
## 554 2020-08-01 Midlands 0
## 555 2020-08-02 Midlands 1
## 556 2020-08-03 Midlands 2
## 557 2020-08-04 Midlands 1
## 558 2020-08-05 Midlands 1
## 559 2020-08-06 Midlands 0
## 560 2020-08-07 Midlands 3
## 561 2020-08-08 Midlands 2
## 562 2020-08-09 Midlands 0
## 563 2020-08-10 Midlands 0
## 564 2020-08-11 Midlands 2
## 565 2020-08-12 Midlands 0
## 566 2020-08-13 Midlands 0
## 567 2020-08-14 Midlands 0
## 568 2020-08-15 Midlands 1
## 569 2020-08-16 Midlands 0
## 570 2020-08-17 Midlands 0
## 571 2020-08-18 Midlands 0
## 572 2020-08-19 Midlands 0
## 573 2020-08-20 Midlands 0
## 574 2020-08-21 Midlands 1
## 575 2020-08-22 Midlands 0
## 576 2020-08-23 Midlands 0
## 577 2020-08-24 Midlands 0
## 578 2020-08-25 Midlands 2
## 579 2020-08-26 Midlands 3
## 580 2020-08-27 Midlands 2
## 581 2020-08-28 Midlands 1
## 582 2020-08-29 Midlands 0
## 583 2020-08-30 Midlands 2
## 584 2020-08-31 Midlands 1
## 585 2020-09-01 Midlands 0
## 586 2020-09-02 Midlands 2
## 587 2020-09-03 Midlands 0
## 588 2020-09-04 Midlands 0
## 589 2020-09-05 Midlands 0
## 590 2020-09-06 Midlands 1
## 591 2020-09-07 Midlands 1
## 592 2020-09-08 Midlands 3
## 593 2020-09-09 Midlands 0
## 594 2020-09-10 Midlands 1
## 595 2020-09-11 Midlands 1
## 596 2020-09-12 Midlands 2
## 597 2020-09-13 Midlands 4
## 598 2020-09-14 Midlands 1
## 599 2020-09-15 Midlands 2
## 600 2020-09-16 Midlands 1
## 601 2020-03-01 North East and Yorkshire 0
## 602 2020-03-02 North East and Yorkshire 0
## 603 2020-03-03 North East and Yorkshire 0
## 604 2020-03-04 North East and Yorkshire 0
## 605 2020-03-05 North East and Yorkshire 0
## 606 2020-03-06 North East and Yorkshire 0
## 607 2020-03-07 North East and Yorkshire 0
## 608 2020-03-08 North East and Yorkshire 0
## 609 2020-03-09 North East and Yorkshire 0
## 610 2020-03-10 North East and Yorkshire 0
## 611 2020-03-11 North East and Yorkshire 0
## 612 2020-03-12 North East and Yorkshire 0
## 613 2020-03-13 North East and Yorkshire 0
## 614 2020-03-14 North East and Yorkshire 0
## 615 2020-03-15 North East and Yorkshire 2
## 616 2020-03-16 North East and Yorkshire 3
## 617 2020-03-17 North East and Yorkshire 1
## 618 2020-03-18 North East and Yorkshire 2
## 619 2020-03-19 North East and Yorkshire 6
## 620 2020-03-20 North East and Yorkshire 5
## 621 2020-03-21 North East and Yorkshire 6
## 622 2020-03-22 North East and Yorkshire 7
## 623 2020-03-23 North East and Yorkshire 9
## 624 2020-03-24 North East and Yorkshire 8
## 625 2020-03-25 North East and Yorkshire 18
## 626 2020-03-26 North East and Yorkshire 21
## 627 2020-03-27 North East and Yorkshire 28
## 628 2020-03-28 North East and Yorkshire 35
## 629 2020-03-29 North East and Yorkshire 38
## 630 2020-03-30 North East and Yorkshire 64
## 631 2020-03-31 North East and Yorkshire 60
## 632 2020-04-01 North East and Yorkshire 67
## 633 2020-04-02 North East and Yorkshire 75
## 634 2020-04-03 North East and Yorkshire 100
## 635 2020-04-04 North East and Yorkshire 105
## 636 2020-04-05 North East and Yorkshire 92
## 637 2020-04-06 North East and Yorkshire 96
## 638 2020-04-07 North East and Yorkshire 102
## 639 2020-04-08 North East and Yorkshire 107
## 640 2020-04-09 North East and Yorkshire 111
## 641 2020-04-10 North East and Yorkshire 117
## 642 2020-04-11 North East and Yorkshire 98
## 643 2020-04-12 North East and Yorkshire 84
## 644 2020-04-13 North East and Yorkshire 94
## 645 2020-04-14 North East and Yorkshire 107
## 646 2020-04-15 North East and Yorkshire 96
## 647 2020-04-16 North East and Yorkshire 103
## 648 2020-04-17 North East and Yorkshire 88
## 649 2020-04-18 North East and Yorkshire 95
## 650 2020-04-19 North East and Yorkshire 88
## 651 2020-04-20 North East and Yorkshire 100
## 652 2020-04-21 North East and Yorkshire 76
## 653 2020-04-22 North East and Yorkshire 84
## 654 2020-04-23 North East and Yorkshire 63
## 655 2020-04-24 North East and Yorkshire 72
## 656 2020-04-25 North East and Yorkshire 69
## 657 2020-04-26 North East and Yorkshire 65
## 658 2020-04-27 North East and Yorkshire 65
## 659 2020-04-28 North East and Yorkshire 57
## 660 2020-04-29 North East and Yorkshire 69
## 661 2020-04-30 North East and Yorkshire 57
## 662 2020-05-01 North East and Yorkshire 64
## 663 2020-05-02 North East and Yorkshire 48
## 664 2020-05-03 North East and Yorkshire 40
## 665 2020-05-04 North East and Yorkshire 49
## 666 2020-05-05 North East and Yorkshire 40
## 667 2020-05-06 North East and Yorkshire 51
## 668 2020-05-07 North East and Yorkshire 45
## 669 2020-05-08 North East and Yorkshire 42
## 670 2020-05-09 North East and Yorkshire 44
## 671 2020-05-10 North East and Yorkshire 40
## 672 2020-05-11 North East and Yorkshire 29
## 673 2020-05-12 North East and Yorkshire 27
## 674 2020-05-13 North East and Yorkshire 28
## 675 2020-05-14 North East and Yorkshire 31
## 676 2020-05-15 North East and Yorkshire 32
## 677 2020-05-16 North East and Yorkshire 35
## 678 2020-05-17 North East and Yorkshire 26
## 679 2020-05-18 North East and Yorkshire 30
## 680 2020-05-19 North East and Yorkshire 27
## 681 2020-05-20 North East and Yorkshire 22
## 682 2020-05-21 North East and Yorkshire 33
## 683 2020-05-22 North East and Yorkshire 22
## 684 2020-05-23 North East and Yorkshire 18
## 685 2020-05-24 North East and Yorkshire 26
## 686 2020-05-25 North East and Yorkshire 21
## 687 2020-05-26 North East and Yorkshire 21
## 688 2020-05-27 North East and Yorkshire 22
## 689 2020-05-28 North East and Yorkshire 21
## 690 2020-05-29 North East and Yorkshire 25
## 691 2020-05-30 North East and Yorkshire 20
## 692 2020-05-31 North East and Yorkshire 20
## 693 2020-06-01 North East and Yorkshire 17
## 694 2020-06-02 North East and Yorkshire 23
## 695 2020-06-03 North East and Yorkshire 23
## 696 2020-06-04 North East and Yorkshire 17
## 697 2020-06-05 North East and Yorkshire 18
## 698 2020-06-06 North East and Yorkshire 21
## 699 2020-06-07 North East and Yorkshire 14
## 700 2020-06-08 North East and Yorkshire 11
## 701 2020-06-09 North East and Yorkshire 12
## 702 2020-06-10 North East and Yorkshire 19
## 703 2020-06-11 North East and Yorkshire 7
## 704 2020-06-12 North East and Yorkshire 9
## 705 2020-06-13 North East and Yorkshire 10
## 706 2020-06-14 North East and Yorkshire 11
## 707 2020-06-15 North East and Yorkshire 9
## 708 2020-06-16 North East and Yorkshire 10
## 709 2020-06-17 North East and Yorkshire 9
## 710 2020-06-18 North East and Yorkshire 11
## 711 2020-06-19 North East and Yorkshire 6
## 712 2020-06-20 North East and Yorkshire 5
## 713 2020-06-21 North East and Yorkshire 4
## 714 2020-06-22 North East and Yorkshire 7
## 715 2020-06-23 North East and Yorkshire 8
## 716 2020-06-24 North East and Yorkshire 10
## 717 2020-06-25 North East and Yorkshire 4
## 718 2020-06-26 North East and Yorkshire 8
## 719 2020-06-27 North East and Yorkshire 4
## 720 2020-06-28 North East and Yorkshire 5
## 721 2020-06-29 North East and Yorkshire 2
## 722 2020-06-30 North East and Yorkshire 7
## 723 2020-07-01 North East and Yorkshire 1
## 724 2020-07-02 North East and Yorkshire 4
## 725 2020-07-03 North East and Yorkshire 4
## 726 2020-07-04 North East and Yorkshire 4
## 727 2020-07-05 North East and Yorkshire 3
## 728 2020-07-06 North East and Yorkshire 2
## 729 2020-07-07 North East and Yorkshire 3
## 730 2020-07-08 North East and Yorkshire 3
## 731 2020-07-09 North East and Yorkshire 0
## 732 2020-07-10 North East and Yorkshire 3
## 733 2020-07-11 North East and Yorkshire 1
## 734 2020-07-12 North East and Yorkshire 4
## 735 2020-07-13 North East and Yorkshire 1
## 736 2020-07-14 North East and Yorkshire 1
## 737 2020-07-15 North East and Yorkshire 2
## 738 2020-07-16 North East and Yorkshire 3
## 739 2020-07-17 North East and Yorkshire 1
## 740 2020-07-18 North East and Yorkshire 2
## 741 2020-07-19 North East and Yorkshire 2
## 742 2020-07-20 North East and Yorkshire 1
## 743 2020-07-21 North East and Yorkshire 1
## 744 2020-07-22 North East and Yorkshire 6
## 745 2020-07-23 North East and Yorkshire 0
## 746 2020-07-24 North East and Yorkshire 1
## 747 2020-07-25 North East and Yorkshire 5
## 748 2020-07-26 North East and Yorkshire 1
## 749 2020-07-27 North East and Yorkshire 0
## 750 2020-07-28 North East and Yorkshire 2
## 751 2020-07-29 North East and Yorkshire 1
## 752 2020-07-30 North East and Yorkshire 0
## 753 2020-07-31 North East and Yorkshire 1
## 754 2020-08-01 North East and Yorkshire 3
## 755 2020-08-02 North East and Yorkshire 2
## 756 2020-08-03 North East and Yorkshire 1
## 757 2020-08-04 North East and Yorkshire 2
## 758 2020-08-05 North East and Yorkshire 1
## 759 2020-08-06 North East and Yorkshire 4
## 760 2020-08-07 North East and Yorkshire 0
## 761 2020-08-08 North East and Yorkshire 1
## 762 2020-08-09 North East and Yorkshire 2
## 763 2020-08-10 North East and Yorkshire 2
## 764 2020-08-11 North East and Yorkshire 2
## 765 2020-08-12 North East and Yorkshire 2
## 766 2020-08-13 North East and Yorkshire 0
## 767 2020-08-14 North East and Yorkshire 1
## 768 2020-08-15 North East and Yorkshire 1
## 769 2020-08-16 North East and Yorkshire 0
## 770 2020-08-17 North East and Yorkshire 4
## 771 2020-08-18 North East and Yorkshire 1
## 772 2020-08-19 North East and Yorkshire 0
## 773 2020-08-20 North East and Yorkshire 0
## 774 2020-08-21 North East and Yorkshire 1
## 775 2020-08-22 North East and Yorkshire 1
## 776 2020-08-23 North East and Yorkshire 2
## 777 2020-08-24 North East and Yorkshire 0
## 778 2020-08-25 North East and Yorkshire 1
## 779 2020-08-26 North East and Yorkshire 2
## 780 2020-08-27 North East and Yorkshire 1
## 781 2020-08-28 North East and Yorkshire 0
## 782 2020-08-29 North East and Yorkshire 1
## 783 2020-08-30 North East and Yorkshire 0
## 784 2020-08-31 North East and Yorkshire 0
## 785 2020-09-01 North East and Yorkshire 2
## 786 2020-09-02 North East and Yorkshire 3
## 787 2020-09-03 North East and Yorkshire 0
## 788 2020-09-04 North East and Yorkshire 0
## 789 2020-09-05 North East and Yorkshire 2
## 790 2020-09-06 North East and Yorkshire 1
## 791 2020-09-07 North East and Yorkshire 0
## 792 2020-09-08 North East and Yorkshire 1
## 793 2020-09-09 North East and Yorkshire 2
## 794 2020-09-10 North East and Yorkshire 0
## 795 2020-09-11 North East and Yorkshire 3
## 796 2020-09-12 North East and Yorkshire 1
## 797 2020-09-13 North East and Yorkshire 3
## 798 2020-09-14 North East and Yorkshire 3
## 799 2020-09-15 North East and Yorkshire 3
## 800 2020-09-16 North East and Yorkshire 2
## 801 2020-03-01 North West 0
## 802 2020-03-02 North West 0
## 803 2020-03-03 North West 0
## 804 2020-03-04 North West 0
## 805 2020-03-05 North West 1
## 806 2020-03-06 North West 0
## 807 2020-03-07 North West 0
## 808 2020-03-08 North West 1
## 809 2020-03-09 North West 0
## 810 2020-03-10 North West 0
## 811 2020-03-11 North West 0
## 812 2020-03-12 North West 2
## 813 2020-03-13 North West 3
## 814 2020-03-14 North West 1
## 815 2020-03-15 North West 4
## 816 2020-03-16 North West 2
## 817 2020-03-17 North West 4
## 818 2020-03-18 North West 6
## 819 2020-03-19 North West 7
## 820 2020-03-20 North West 10
## 821 2020-03-21 North West 11
## 822 2020-03-22 North West 13
## 823 2020-03-23 North West 15
## 824 2020-03-24 North West 21
## 825 2020-03-25 North West 21
## 826 2020-03-26 North West 29
## 827 2020-03-27 North West 36
## 828 2020-03-28 North West 28
## 829 2020-03-29 North West 46
## 830 2020-03-30 North West 67
## 831 2020-03-31 North West 52
## 832 2020-04-01 North West 86
## 833 2020-04-02 North West 96
## 834 2020-04-03 North West 95
## 835 2020-04-04 North West 98
## 836 2020-04-05 North West 102
## 837 2020-04-06 North West 100
## 838 2020-04-07 North West 135
## 839 2020-04-08 North West 127
## 840 2020-04-09 North West 119
## 841 2020-04-10 North West 117
## 842 2020-04-11 North West 138
## 843 2020-04-12 North West 125
## 844 2020-04-13 North West 129
## 845 2020-04-14 North West 131
## 846 2020-04-15 North West 114
## 847 2020-04-16 North West 135
## 848 2020-04-17 North West 98
## 849 2020-04-18 North West 113
## 850 2020-04-19 North West 71
## 851 2020-04-20 North West 83
## 852 2020-04-21 North West 76
## 853 2020-04-22 North West 86
## 854 2020-04-23 North West 85
## 855 2020-04-24 North West 66
## 856 2020-04-25 North West 66
## 857 2020-04-26 North West 55
## 858 2020-04-27 North West 54
## 859 2020-04-28 North West 57
## 860 2020-04-29 North West 63
## 861 2020-04-30 North West 59
## 862 2020-05-01 North West 45
## 863 2020-05-02 North West 56
## 864 2020-05-03 North West 55
## 865 2020-05-04 North West 48
## 866 2020-05-05 North West 48
## 867 2020-05-06 North West 44
## 868 2020-05-07 North West 49
## 869 2020-05-08 North West 42
## 870 2020-05-09 North West 31
## 871 2020-05-10 North West 42
## 872 2020-05-11 North West 35
## 873 2020-05-12 North West 38
## 874 2020-05-13 North West 25
## 875 2020-05-14 North West 26
## 876 2020-05-15 North West 33
## 877 2020-05-16 North West 32
## 878 2020-05-17 North West 24
## 879 2020-05-18 North West 31
## 880 2020-05-19 North West 35
## 881 2020-05-20 North West 27
## 882 2020-05-21 North West 27
## 883 2020-05-22 North West 26
## 884 2020-05-23 North West 31
## 885 2020-05-24 North West 26
## 886 2020-05-25 North West 31
## 887 2020-05-26 North West 27
## 888 2020-05-27 North West 27
## 889 2020-05-28 North West 28
## 890 2020-05-29 North West 20
## 891 2020-05-30 North West 19
## 892 2020-05-31 North West 13
## 893 2020-06-01 North West 12
## 894 2020-06-02 North West 27
## 895 2020-06-03 North West 22
## 896 2020-06-04 North West 22
## 897 2020-06-05 North West 16
## 898 2020-06-06 North West 26
## 899 2020-06-07 North West 20
## 900 2020-06-08 North West 23
## 901 2020-06-09 North West 17
## 902 2020-06-10 North West 16
## 903 2020-06-11 North West 16
## 904 2020-06-12 North West 11
## 905 2020-06-13 North West 10
## 906 2020-06-14 North West 15
## 907 2020-06-15 North West 16
## 908 2020-06-16 North West 16
## 909 2020-06-17 North West 13
## 910 2020-06-18 North West 14
## 911 2020-06-19 North West 7
## 912 2020-06-20 North West 11
## 913 2020-06-21 North West 8
## 914 2020-06-22 North West 11
## 915 2020-06-23 North West 13
## 916 2020-06-24 North West 13
## 917 2020-06-25 North West 15
## 918 2020-06-26 North West 6
## 919 2020-06-27 North West 7
## 920 2020-06-28 North West 9
## 921 2020-06-29 North West 9
## 922 2020-06-30 North West 7
## 923 2020-07-01 North West 3
## 924 2020-07-02 North West 6
## 925 2020-07-03 North West 7
## 926 2020-07-04 North West 4
## 927 2020-07-05 North West 6
## 928 2020-07-06 North West 9
## 929 2020-07-07 North West 8
## 930 2020-07-08 North West 5
## 931 2020-07-09 North West 10
## 932 2020-07-10 North West 2
## 933 2020-07-11 North West 5
## 934 2020-07-12 North West 0
## 935 2020-07-13 North West 6
## 936 2020-07-14 North West 4
## 937 2020-07-15 North West 5
## 938 2020-07-16 North West 2
## 939 2020-07-17 North West 4
## 940 2020-07-18 North West 5
## 941 2020-07-19 North West 3
## 942 2020-07-20 North West 0
## 943 2020-07-21 North West 2
## 944 2020-07-22 North West 3
## 945 2020-07-23 North West 3
## 946 2020-07-24 North West 1
## 947 2020-07-25 North West 1
## 948 2020-07-26 North West 3
## 949 2020-07-27 North West 1
## 950 2020-07-28 North West 1
## 951 2020-07-29 North West 2
## 952 2020-07-30 North West 2
## 953 2020-07-31 North West 0
## 954 2020-08-01 North West 2
## 955 2020-08-02 North West 1
## 956 2020-08-03 North West 8
## 957 2020-08-04 North West 3
## 958 2020-08-05 North West 2
## 959 2020-08-06 North West 2
## 960 2020-08-07 North West 2
## 961 2020-08-08 North West 2
## 962 2020-08-09 North West 3
## 963 2020-08-10 North West 2
## 964 2020-08-11 North West 3
## 965 2020-08-12 North West 0
## 966 2020-08-13 North West 2
## 967 2020-08-14 North West 2
## 968 2020-08-15 North West 6
## 969 2020-08-16 North West 2
## 970 2020-08-17 North West 1
## 971 2020-08-18 North West 2
## 972 2020-08-19 North West 0
## 973 2020-08-20 North West 1
## 974 2020-08-21 North West 4
## 975 2020-08-22 North West 3
## 976 2020-08-23 North West 4
## 977 2020-08-24 North West 4
## 978 2020-08-25 North West 3
## 979 2020-08-26 North West 4
## 980 2020-08-27 North West 1
## 981 2020-08-28 North West 2
## 982 2020-08-29 North West 0
## 983 2020-08-30 North West 2
## 984 2020-08-31 North West 2
## 985 2020-09-01 North West 0
## 986 2020-09-02 North West 2
## 987 2020-09-03 North West 1
## 988 2020-09-04 North West 3
## 989 2020-09-05 North West 6
## 990 2020-09-06 North West 1
## 991 2020-09-07 North West 8
## 992 2020-09-08 North West 5
## 993 2020-09-09 North West 4
## 994 2020-09-10 North West 4
## 995 2020-09-11 North West 0
## 996 2020-09-12 North West 3
## 997 2020-09-13 North West 1
## 998 2020-09-14 North West 3
## 999 2020-09-15 North West 1
## 1000 2020-09-16 North West 2
## 1001 2020-03-01 South East 0
## 1002 2020-03-02 South East 0
## 1003 2020-03-03 South East 1
## 1004 2020-03-04 South East 0
## 1005 2020-03-05 South East 1
## 1006 2020-03-06 South East 0
## 1007 2020-03-07 South East 0
## 1008 2020-03-08 South East 1
## 1009 2020-03-09 South East 1
## 1010 2020-03-10 South East 1
## 1011 2020-03-11 South East 1
## 1012 2020-03-12 South East 0
## 1013 2020-03-13 South East 1
## 1014 2020-03-14 South East 1
## 1015 2020-03-15 South East 5
## 1016 2020-03-16 South East 8
## 1017 2020-03-17 South East 7
## 1018 2020-03-18 South East 10
## 1019 2020-03-19 South East 9
## 1020 2020-03-20 South East 13
## 1021 2020-03-21 South East 7
## 1022 2020-03-22 South East 25
## 1023 2020-03-23 South East 20
## 1024 2020-03-24 South East 22
## 1025 2020-03-25 South East 29
## 1026 2020-03-26 South East 35
## 1027 2020-03-27 South East 35
## 1028 2020-03-28 South East 36
## 1029 2020-03-29 South East 55
## 1030 2020-03-30 South East 58
## 1031 2020-03-31 South East 65
## 1032 2020-04-01 South East 66
## 1033 2020-04-02 South East 55
## 1034 2020-04-03 South East 72
## 1035 2020-04-04 South East 80
## 1036 2020-04-05 South East 82
## 1037 2020-04-06 South East 88
## 1038 2020-04-07 South East 100
## 1039 2020-04-08 South East 83
## 1040 2020-04-09 South East 104
## 1041 2020-04-10 South East 88
## 1042 2020-04-11 South East 88
## 1043 2020-04-12 South East 88
## 1044 2020-04-13 South East 84
## 1045 2020-04-14 South East 65
## 1046 2020-04-15 South East 72
## 1047 2020-04-16 South East 56
## 1048 2020-04-17 South East 86
## 1049 2020-04-18 South East 57
## 1050 2020-04-19 South East 70
## 1051 2020-04-20 South East 87
## 1052 2020-04-21 South East 51
## 1053 2020-04-22 South East 54
## 1054 2020-04-23 South East 57
## 1055 2020-04-24 South East 64
## 1056 2020-04-25 South East 51
## 1057 2020-04-26 South East 51
## 1058 2020-04-27 South East 41
## 1059 2020-04-28 South East 40
## 1060 2020-04-29 South East 47
## 1061 2020-04-30 South East 29
## 1062 2020-05-01 South East 37
## 1063 2020-05-02 South East 36
## 1064 2020-05-03 South East 17
## 1065 2020-05-04 South East 35
## 1066 2020-05-05 South East 29
## 1067 2020-05-06 South East 25
## 1068 2020-05-07 South East 27
## 1069 2020-05-08 South East 26
## 1070 2020-05-09 South East 28
## 1071 2020-05-10 South East 19
## 1072 2020-05-11 South East 25
## 1073 2020-05-12 South East 27
## 1074 2020-05-13 South East 18
## 1075 2020-05-14 South East 32
## 1076 2020-05-15 South East 25
## 1077 2020-05-16 South East 22
## 1078 2020-05-17 South East 18
## 1079 2020-05-18 South East 22
## 1080 2020-05-19 South East 12
## 1081 2020-05-20 South East 22
## 1082 2020-05-21 South East 15
## 1083 2020-05-22 South East 17
## 1084 2020-05-23 South East 21
## 1085 2020-05-24 South East 17
## 1086 2020-05-25 South East 13
## 1087 2020-05-26 South East 19
## 1088 2020-05-27 South East 19
## 1089 2020-05-28 South East 12
## 1090 2020-05-29 South East 22
## 1091 2020-05-30 South East 8
## 1092 2020-05-31 South East 12
## 1093 2020-06-01 South East 11
## 1094 2020-06-02 South East 13
## 1095 2020-06-03 South East 18
## 1096 2020-06-04 South East 11
## 1097 2020-06-05 South East 11
## 1098 2020-06-06 South East 10
## 1099 2020-06-07 South East 12
## 1100 2020-06-08 South East 8
## 1101 2020-06-09 South East 10
## 1102 2020-06-10 South East 11
## 1103 2020-06-11 South East 5
## 1104 2020-06-12 South East 6
## 1105 2020-06-13 South East 7
## 1106 2020-06-14 South East 7
## 1107 2020-06-15 South East 8
## 1108 2020-06-16 South East 14
## 1109 2020-06-17 South East 9
## 1110 2020-06-18 South East 4
## 1111 2020-06-19 South East 7
## 1112 2020-06-20 South East 5
## 1113 2020-06-21 South East 3
## 1114 2020-06-22 South East 2
## 1115 2020-06-23 South East 9
## 1116 2020-06-24 South East 7
## 1117 2020-06-25 South East 5
## 1118 2020-06-26 South East 8
## 1119 2020-06-27 South East 9
## 1120 2020-06-28 South East 6
## 1121 2020-06-29 South East 5
## 1122 2020-06-30 South East 5
## 1123 2020-07-01 South East 2
## 1124 2020-07-02 South East 8
## 1125 2020-07-03 South East 3
## 1126 2020-07-04 South East 6
## 1127 2020-07-05 South East 5
## 1128 2020-07-06 South East 4
## 1129 2020-07-07 South East 6
## 1130 2020-07-08 South East 3
## 1131 2020-07-09 South East 7
## 1132 2020-07-10 South East 3
## 1133 2020-07-11 South East 4
## 1134 2020-07-12 South East 4
## 1135 2020-07-13 South East 5
## 1136 2020-07-14 South East 5
## 1137 2020-07-15 South East 6
## 1138 2020-07-16 South East 3
## 1139 2020-07-17 South East 1
## 1140 2020-07-18 South East 5
## 1141 2020-07-19 South East 2
## 1142 2020-07-20 South East 6
## 1143 2020-07-21 South East 4
## 1144 2020-07-22 South East 2
## 1145 2020-07-23 South East 3
## 1146 2020-07-24 South East 1
## 1147 2020-07-25 South East 1
## 1148 2020-07-26 South East 3
## 1149 2020-07-27 South East 1
## 1150 2020-07-28 South East 3
## 1151 2020-07-29 South East 2
## 1152 2020-07-30 South East 3
## 1153 2020-07-31 South East 1
## 1154 2020-08-01 South East 2
## 1155 2020-08-02 South East 4
## 1156 2020-08-03 South East 0
## 1157 2020-08-04 South East 0
## 1158 2020-08-05 South East 0
## 1159 2020-08-06 South East 2
## 1160 2020-08-07 South East 0
## 1161 2020-08-08 South East 2
## 1162 2020-08-09 South East 0
## 1163 2020-08-10 South East 2
## 1164 2020-08-11 South East 1
## 1165 2020-08-12 South East 1
## 1166 2020-08-13 South East 0
## 1167 2020-08-14 South East 0
## 1168 2020-08-15 South East 2
## 1169 2020-08-16 South East 1
## 1170 2020-08-17 South East 0
## 1171 2020-08-18 South East 2
## 1172 2020-08-19 South East 1
## 1173 2020-08-20 South East 0
## 1174 2020-08-21 South East 0
## 1175 2020-08-22 South East 0
## 1176 2020-08-23 South East 1
## 1177 2020-08-24 South East 0
## 1178 2020-08-25 South East 1
## 1179 2020-08-26 South East 0
## 1180 2020-08-27 South East 1
## 1181 2020-08-28 South East 1
## 1182 2020-08-29 South East 1
## 1183 2020-08-30 South East 0
## 1184 2020-08-31 South East 2
## 1185 2020-09-01 South East 1
## 1186 2020-09-02 South East 1
## 1187 2020-09-03 South East 0
## 1188 2020-09-04 South East 1
## 1189 2020-09-05 South East 0
## 1190 2020-09-06 South East 1
## 1191 2020-09-07 South East 0
## 1192 2020-09-08 South East 0
## 1193 2020-09-09 South East 0
## 1194 2020-09-10 South East 1
## 1195 2020-09-11 South East 1
## 1196 2020-09-12 South East 0
## 1197 2020-09-13 South East 2
## 1198 2020-09-14 South East 1
## 1199 2020-09-15 South East 1
## 1200 2020-09-16 South East 1
## 1201 2020-03-01 South West 0
## 1202 2020-03-02 South West 0
## 1203 2020-03-03 South West 0
## 1204 2020-03-04 South West 0
## 1205 2020-03-05 South West 0
## 1206 2020-03-06 South West 0
## 1207 2020-03-07 South West 0
## 1208 2020-03-08 South West 0
## 1209 2020-03-09 South West 0
## 1210 2020-03-10 South West 0
## 1211 2020-03-11 South West 1
## 1212 2020-03-12 South West 0
## 1213 2020-03-13 South West 0
## 1214 2020-03-14 South West 1
## 1215 2020-03-15 South West 0
## 1216 2020-03-16 South West 0
## 1217 2020-03-17 South West 2
## 1218 2020-03-18 South West 2
## 1219 2020-03-19 South West 4
## 1220 2020-03-20 South West 3
## 1221 2020-03-21 South West 6
## 1222 2020-03-22 South West 7
## 1223 2020-03-23 South West 8
## 1224 2020-03-24 South West 7
## 1225 2020-03-25 South West 9
## 1226 2020-03-26 South West 11
## 1227 2020-03-27 South West 13
## 1228 2020-03-28 South West 21
## 1229 2020-03-29 South West 18
## 1230 2020-03-30 South West 23
## 1231 2020-03-31 South West 23
## 1232 2020-04-01 South West 21
## 1233 2020-04-02 South West 23
## 1234 2020-04-03 South West 30
## 1235 2020-04-04 South West 42
## 1236 2020-04-05 South West 32
## 1237 2020-04-06 South West 34
## 1238 2020-04-07 South West 39
## 1239 2020-04-08 South West 47
## 1240 2020-04-09 South West 24
## 1241 2020-04-10 South West 46
## 1242 2020-04-11 South West 43
## 1243 2020-04-12 South West 23
## 1244 2020-04-13 South West 27
## 1245 2020-04-14 South West 24
## 1246 2020-04-15 South West 32
## 1247 2020-04-16 South West 29
## 1248 2020-04-17 South West 33
## 1249 2020-04-18 South West 25
## 1250 2020-04-19 South West 31
## 1251 2020-04-20 South West 26
## 1252 2020-04-21 South West 26
## 1253 2020-04-22 South West 23
## 1254 2020-04-23 South West 17
## 1255 2020-04-24 South West 19
## 1256 2020-04-25 South West 15
## 1257 2020-04-26 South West 27
## 1258 2020-04-27 South West 13
## 1259 2020-04-28 South West 17
## 1260 2020-04-29 South West 15
## 1261 2020-04-30 South West 26
## 1262 2020-05-01 South West 6
## 1263 2020-05-02 South West 7
## 1264 2020-05-03 South West 10
## 1265 2020-05-04 South West 17
## 1266 2020-05-05 South West 14
## 1267 2020-05-06 South West 19
## 1268 2020-05-07 South West 16
## 1269 2020-05-08 South West 6
## 1270 2020-05-09 South West 11
## 1271 2020-05-10 South West 5
## 1272 2020-05-11 South West 8
## 1273 2020-05-12 South West 7
## 1274 2020-05-13 South West 7
## 1275 2020-05-14 South West 6
## 1276 2020-05-15 South West 4
## 1277 2020-05-16 South West 4
## 1278 2020-05-17 South West 6
## 1279 2020-05-18 South West 4
## 1280 2020-05-19 South West 6
## 1281 2020-05-20 South West 1
## 1282 2020-05-21 South West 9
## 1283 2020-05-22 South West 7
## 1284 2020-05-23 South West 6
## 1285 2020-05-24 South West 3
## 1286 2020-05-25 South West 8
## 1287 2020-05-26 South West 11
## 1288 2020-05-27 South West 5
## 1289 2020-05-28 South West 10
## 1290 2020-05-29 South West 7
## 1291 2020-05-30 South West 3
## 1292 2020-05-31 South West 2
## 1293 2020-06-01 South West 7
## 1294 2020-06-02 South West 2
## 1295 2020-06-03 South West 7
## 1296 2020-06-04 South West 2
## 1297 2020-06-05 South West 2
## 1298 2020-06-06 South West 1
## 1299 2020-06-07 South West 3
## 1300 2020-06-08 South West 3
## 1301 2020-06-09 South West 0
## 1302 2020-06-10 South West 1
## 1303 2020-06-11 South West 2
## 1304 2020-06-12 South West 2
## 1305 2020-06-13 South West 2
## 1306 2020-06-14 South West 0
## 1307 2020-06-15 South West 2
## 1308 2020-06-16 South West 2
## 1309 2020-06-17 South West 0
## 1310 2020-06-18 South West 0
## 1311 2020-06-19 South West 0
## 1312 2020-06-20 South West 2
## 1313 2020-06-21 South West 0
## 1314 2020-06-22 South West 1
## 1315 2020-06-23 South West 1
## 1316 2020-06-24 South West 1
## 1317 2020-06-25 South West 0
## 1318 2020-06-26 South West 3
## 1319 2020-06-27 South West 0
## 1320 2020-06-28 South West 0
## 1321 2020-06-29 South West 1
## 1322 2020-06-30 South West 0
## 1323 2020-07-01 South West 0
## 1324 2020-07-02 South West 0
## 1325 2020-07-03 South West 0
## 1326 2020-07-04 South West 0
## 1327 2020-07-05 South West 1
## 1328 2020-07-06 South West 0
## 1329 2020-07-07 South West 0
## 1330 2020-07-08 South West 2
## 1331 2020-07-09 South West 0
## 1332 2020-07-10 South West 1
## 1333 2020-07-11 South West 0
## 1334 2020-07-12 South West 0
## 1335 2020-07-13 South West 1
## 1336 2020-07-14 South West 0
## 1337 2020-07-15 South West 0
## 1338 2020-07-16 South West 0
## 1339 2020-07-17 South West 1
## 1340 2020-07-18 South West 0
## 1341 2020-07-19 South West 0
## 1342 2020-07-20 South West 0
## 1343 2020-07-21 South West 0
## 1344 2020-07-22 South West 0
## 1345 2020-07-23 South West 0
## 1346 2020-07-24 South West 0
## 1347 2020-07-25 South West 0
## 1348 2020-07-26 South West 0
## 1349 2020-07-27 South West 0
## 1350 2020-07-28 South West 0
## 1351 2020-07-29 South West 0
## 1352 2020-07-30 South West 1
## 1353 2020-07-31 South West 0
## 1354 2020-08-01 South West 0
## 1355 2020-08-02 South West 0
## 1356 2020-08-03 South West 0
## 1357 2020-08-04 South West 0
## 1358 2020-08-05 South West 0
## 1359 2020-08-06 South West 0
## 1360 2020-08-07 South West 0
## 1361 2020-08-08 South West 0
## 1362 2020-08-09 South West 0
## 1363 2020-08-10 South West 0
## 1364 2020-08-11 South West 0
## 1365 2020-08-12 South West 0
## 1366 2020-08-13 South West 0
## 1367 2020-08-14 South West 1
## 1368 2020-08-15 South West 0
## 1369 2020-08-16 South West 0
## 1370 2020-08-17 South West 2
## 1371 2020-08-18 South West 0
## 1372 2020-08-19 South West 0
## 1373 2020-08-20 South West 0
## 1374 2020-08-21 South West 0
## 1375 2020-08-22 South West 0
## 1376 2020-08-23 South West 0
## 1377 2020-08-24 South West 0
## 1378 2020-08-25 South West 1
## 1379 2020-08-26 South West 0
## 1380 2020-08-27 South West 1
## 1381 2020-08-28 South West 0
## 1382 2020-08-29 South West 0
## 1383 2020-08-30 South West 0
## 1384 2020-08-31 South West 0
## 1385 2020-09-01 South West 0
## 1386 2020-09-02 South West 0
## 1387 2020-09-03 South West 0
## 1388 2020-09-04 South West 0
## 1389 2020-09-05 South West 0
## 1390 2020-09-06 South West 0
## 1391 2020-09-07 South West 0
## 1392 2020-09-08 South West 1
## 1393 2020-09-09 South West 0
## 1394 2020-09-10 South West 0
## 1395 2020-09-11 South West 0
## 1396 2020-09-12 South West 0
## 1397 2020-09-13 South West 0
## 1398 2020-09-14 South West 0
## 1399 2020-09-15 South West 0
## 1400 2020-09-16 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Thursday 17 Sep 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -20.001 -7.693 -2.981 4.376 13.437
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.000e+00 8.344e-02 47.94 <2e-16 ***
## note_lag 1.941e-05 8.661e-07 22.41 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 49.90048)
##
## Null deviance: 28531.4 on 138 degrees of freedom
## Residual deviance: 7326.6 on 137 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 54.614303 1.000019
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 46.199105 64.083712
## note_lag 1.000018 1.000021
Rsq(lag_mod)
## [1] 0.7432105
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
Sys.info()
## sysname
## "Darwin"
## release
## "19.6.0"
## version
## "Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64"
## nodename
## "Mac-1600423949392.local"
## machine
## "x86_64"
## login
## "root"
## user
## "runner"
## effective_user
## "runner"6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.3 ggpubr_0.4.0 lubridate_1.7.9
## [4] chngpt_2020.8-29 cyphr_1.1.0 DT_0.15
## [7] kableExtra_1.2.1 janitor_2.0.1 remotes_2.2.0
## [10] projections_0.5.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.2 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.6 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.2 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.2 tibble_3.0.3 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] minqa_1.2.4 colorspace_1.4-1 selectr_0.4-2 ggsignif_0.6.0
## [5] ellipsis_0.3.1 rprojroot_1.3-2 snakecase_0.11.0 fs_1.5.0
## [9] rstudioapi_0.11 farver_2.0.3 fansi_0.4.1 splines_4.0.2
## [13] knitr_1.29 jsonlite_1.7.1 nloptr_1.2.2.2 broom_0.7.0
## [17] dbplyr_1.4.4 compiler_4.0.2 httr_1.4.2 backports_1.1.10
## [21] assertthat_0.2.1 Matrix_1.2-18 cli_2.0.2 htmltools_0.5.0
## [25] tools_4.0.2 gtable_0.3.0 glue_1.4.2 Rcpp_1.0.5
## [29] carData_3.0-4 cellranger_1.1.0 vctrs_0.3.4 nlme_3.1-148
## [33] matchmaker_0.1.1 crosstalk_1.1.0.1 xfun_0.17 openxlsx_4.2.2
## [37] lme4_1.1-23 lifecycle_0.2.0 statmod_1.4.34 rstatix_0.6.0
## [41] MASS_7.3-51.6 scales_1.1.1 hms_0.5.3 sodium_1.1
## [45] yaml_2.2.1 curl_4.3 gridExtra_2.3 stringi_1.5.3
## [49] kyotil_2020.8-22 boot_1.3-25 zip_2.1.1 rlang_0.4.7
## [53] pkgconfig_2.0.3 evaluate_0.14 lattice_0.20-41 htmlwidgets_1.5.1
## [57] labeling_0.3 cowplot_1.1.0 tidyselect_1.1.0 plyr_1.8.6
## [61] magrittr_1.5 R6_2.4.1 generics_0.0.2 DBI_1.1.0
## [65] pillar_1.4.6 haven_2.3.1 foreign_0.8-80 withr_2.2.0
## [69] mgcv_1.8-31 survival_3.1-12 abind_1.4-5 modelr_0.1.8
## [73] crayon_1.3.4 car_3.0-9 utf8_1.1.4 rmarkdown_2.3
## [77] viridis_0.5.1 grid_4.0.2 readxl_1.3.1 data.table_1.13.0
## [81] blob_1.2.1 reprex_0.3.0 digest_0.6.25 webshot_0.5.2
## [85] munsell_0.5.0 viridisLite_0.3.0